Managing Meritocracy: Zohran Mamdani, School Choice, and the Politics of Process

In a city where educational access is routed through systems of transit, testing, and algorithmic assignment, the yellow school bus becomes both a symbol of opportunity and a vehicle of stratification. Photo courtesy of Jason Lawrence.

Can a school fueled by competition ever deliver justice in a public school system built on inequality? In the wake of a mayoral campaign marked by critiques of systemic issues, New York assemblyman Zohran Mamdani argues that the public school system must be reformed by reducing mayoral control and giving parents and teachers a greater voice. The longstanding foundations of New York City’s high school admissions are built on the idea that equity can be achieved by optimizing access to competition. Under the current system, equity is framed as expanding access to competitive admissions, with students expected to prove merit through tests and selective processes. Mamdani seeks to challenge this logic, instead promising to dismantle the competitive structures altogether. This ideological divergence reflects the internal contradiction of Mamdani’s position: to be a democratic socialist within the Democratic Party is to oppose the logic of market competition while remaining bound to institutions that reproduce it.

Nowhere is this dissonance more clearly reflected than in New York City’s high school admissions algorithm, a system hailed for optimizing an engineered simulation of “choice,” yet skewed to reward informational capital and strategic behavior. The city’s algorithm processes ranked preferences on both sides; students rank schools, and schools rank students. It then iteratively matches them to ensure that no student and school pair would prefer one another over their assignment placement. In theory, the system is a model of procedural fairness. However, this fairness relies on actors having roughly equal information and access to resources. In a school system defined by structural inequality and legal limits on race-conscious admissions, algorithmic processes that simulate choice continue to reproduce inequality.

For defenders of the system, the algorithm provides order, efficiency, and transparency. For its critics, it simulates the logic of the free market, while ignoring the structural conditions that shape educational opportunity itself. The central question remains not whether the admission algorithms must be discarded, but where they can be restructured to serve the democratic ends that Mamdani espouses. In a post-affirmative-action legal landscape, where race-conscious criteria face constitutional limits, algorithmic tools may be among the few remaining instruments available for pursuing equity through legally permissible, context-based criteria. The challenge, then, is to reweight and redefine the functions of these tools, and to submit their logic to the communities they govern. 

Though the current admissions algorithm claims neutrality, it operates under conditions that are anything but equal. The Specialized High School Admissions Test (SHSAT), administered once a year to eighth- and ninth-grade students and still used as the sole admissions criterion at nine specialized high schools, exemplifies this logic. While presented as an objective measure of aptitude, it often functions as a proxy for access to early preparation, strategic guidance, and institutional fluency. In 2018, Black and Latino students, who together comprised nearly 70 percent of the public school population, were offered under 10 percent of specialized school offers. By 2024, only 10 Black students were admitted to Stuyvesant High School–one of the city’s most prestigious public schools–out of 744 total offers. Simultaneously, the upcoming digitization of the SHSAT may reduce some logistical barriers, but leaves intact structural inequalities, like access to sustained tutoring, exam–specific curricula, and insider advising, that ultimately determine preparedness. 

Herein lies the irony: Mamdani, whose politics trend toward the deconstruction of hegemonic systems, has endorsed maintaining similar elements in New York City high school admissions—namely, continued SHSAT administration. In doing so, he gestures toward inclusion without directly confronting the infrastructure that governs access. Of course, political compromise is not hypocrisy, and Mamdani may view these tools as pragmatic necessities under existing legal constraints. Still, democratic socialism has historically positioned itself against managerial logics of governance, seeking to expand democratic control rather than replicate technocratic methods under a progressive banner—a tension that remains unresolved within Mamdani's education platform. This contradiction emerges against the backdrop of New York City’s enduring school segregation, where the very mechanisms he proposes to reform continue to reproduce inequality. 

Despite legal intervention, de facto school segregation persists, maintained through spatial coding and policy inertia; redlining and exclusionary zoning have calcified residential patterns that function as a geographic determinant for educational access. The Hecht-Calandra Act, which codifies the SHSAT as the sole admissions criterion for specialized high schools, institutionalizes that sorting mechanism into law. Algorithm inputs—including grades, attendance, and preferences—are loaded dice conditioned by factors such as access to Algebra in 8th grade, to accelerated academic tracks on a broad scale, and to systems-level information that itself reflects geographic disparities in school resources and neighborhood opportunity. Compounding these disparities are factors like PTA fundraising gaps, the spatial clustering of SHSAT prep, and informal advising networks that materially shape application strategy. Efforts to disrupt this pattern, such as former New York City mayor Bill de Blasio’s phased reallocation of seats to top middle school performers, indicate a structural path not yet taken. Mamdani’s platform gestures toward this terrain through audits and a co-governance model of reform, though he has not yet articulated a path to alter the legal or logistical basis of the admissions hierarchy. 

In the aftermath of the Supreme Court's ruling that curtailed explicit race-based admissions, algorithmic equity emerges through proxy structure rather than direct criteria: weightings assigned to ZIP codes, economic need, or ELL status provide statistically permissible avenues for redress, but only within a framework calibrated to avoid constitutional limits. These weightings, while superficially redistributive, function within an input-output schema structured by technocratic goals: modest adjustments in the algorithm’s cost function yield inclusion that, while tangible for some students, stops short of systemic rupture.

Meanwhile, AI-based tools, including essay-scoring LLMs and dropout-likelihood classifiers, are being piloted in districts as efficiency devices, but may widen disparity by replicating human bias in data patterns. Weightings risk becoming little more than moral coefficients, retroactively applied to outputs that remain contingent on structurally unequal inputs. AI, no longer a neutral mediator, assumes the role of adjudicator: its judgments are masked as efficiency, its selections ingrained in objectivity, and its exclusions rendered illegible. Yet, there is a possibility that AI evaluations will deliver substantive equity, and that a system trained on disparity can, through sufficient nuance, unlearn its foundations.

The limitations of algorithmic equity become even more visible when viewed in practice, and the policy frameworks tested in Philadelphia’s selective school admissions offer a cautionary case study. In 2021, the district replaced principal-level discretion with a centralized, computerized lottery. Applicants first needed to meet eligibility criteria—grades, attendance, standardized test percentiles, and an AI-scored essay—alongside a policy designed to expand access for students from six historically underrepresented ZIP codes to Central High School, Julia R. Masterman Laboratory and Demonstration School, George Washington Carver High School of Engineering and Science, and Academy at Palumbo. The goal was to diversify these selective schools. Early evidence suggests the ZIP-code preference boosted Black and Hispanic enrollment at the targeted schools, while the centralized lottery alone had little effect; changes at Central and Masterman were modest. 

Resistance erupted across constituencies: a federal lawsuit alleged reverse discrimination; parents decried a dilution of academic standards; and only one in five surveyed families expressed satisfaction with the system. By 2023, the district revised its approach: eligibility thresholds were adjusted and ranked-choice mechanisms, similar to the preference-optimization in New York, were employed to stabilize placements. This recursive policy spiral of design, backlash, and retrenchment mirrors the structural lesson: without investment in academic support and infrastructural capacity, algorithmic systems merely model equitable structures. Without parallel reform, an optimized algorithm cannot redistribute opportunity. This limitation is evident in Mamdani’s platform, which gestures toward audits and co-governance reforms, but stops short of outlining how admissions law or resource allocation might be restructured to confront segregation directly. 

In a policy terrain where equity is increasingly measured through metrics, youth-led organizations have become the conscience of high school admissions discourse. Teens Take Charge, through its Education Unscreened campaign, has argued for an epistemic shift that centers student experience as paramount, reframing screening as a lived barrier to opportunity. Parallel efforts by IntegrateNYC employed an intergenerational praxis of litigation, organizing, and curriculum co-design to argue for a reconfiguration of power. 

Middle school thus emerges as the most consequential site of intervention, where inequities underwriting high school admissions are first entrenched. Access to algebra in eighth grade, enrollment in accelerated coursework, and sustained academic counseling substantially expand selective placement, yet these opportunities remain stratified by geography and class. A credible reform would seek to guarantee access rather than automatic enrollment, institutionalized through the NYCDOE’s curricular offices or newly constituted state-level equity bodies with authority to reallocate funds. Investments might include subsidized tutoring, expanding eligibility and outreach for advanced courses, and community-based advising beginning in sixth grade, thereby enabling students to opt into accelerated pathways without reproducing the inequities of tracking. Critics may contend that such programs strain limited budgets or risk diluting rigor, but these concerns can be mitigated through targeted rollouts in underserved districts, transparent evaluation of outcomes, and reinvestment of philanthropic and state funds. Without such preparatory measures, redistributive lotteries, like those tested in Philadelphia, remain fragile when grafted onto unequal pipelines. 

Preparatory reform, however, must be paired with a reconfiguration of the admissions algorithm itself. Equity-weighted lotteries, structured by contextual factors such as ZIP code, income, and English-learner status, can increase admission probabilities for underserved groups only if designed with transparency and subject to independent auditing. Review boards could run simulations on synthetic datasets to project outcomes across race, income, and geography, rendering algorithmic equity legible to the public it purports to serve. Funding for such oversight could come from reallocations within the NYCDOE’s technology budget or targeted state innovation grants, though these measures require political will rather than technical adjustment alone. The potential benefits—a legally permissible mechanism for equity in the wake of affirmative action—must be weighed against the risks of backlash and the danger that weightenings devolve into cosmetic nudges absent structural reform. Only through marginal analysis attentive to cost, legitimacy, and distributive consequence can algorithms evolve into engines of substantive equity. 

Ava DiGiuseppe (CC ‘29) is a writer in the CPR New Student Summer Publishing Program. She plans to study political science and classics with an interest in constitutional law, educational equity, and normative foundations of governance. She can be reached at ard2232@columbia.edu.